Abstract

This paper describes a practical method of adjusting existing Institute of Transportation Engineers (ITE) estimates to produce more accurate estimates of motor-vehicle trip-generation at developments in smart-growth areas. Two linear regression equations, one for an A.M. peak-hour adjustment and one for a P.M. peak-hour adjustment, were developed using vehicle trip counts and easily measured site and surrounding area context variables from a sample of 50 smart-growth sites in California. Many of the contextual variables that were associated with lower vehicle trip generation at the smart-growth study sites were correlated. Therefore, variables representing characteristics such as residential population density, employment density, transit service, metered on-street parking, and building setback distance from the sidewalk were combined into a single “smart-growth factor” that was used in the linear regression equations. The A.M. peak-hour and P.M. peak-hour adjustment equations are only appropriate for planning-level analysis at sites in smart-growth areas. In addition, the method is only appropriate for single land uses in several common categories, such as office, mid- to high-density residential, restaurant, and coffee/donut shop. The method uses data from California, but the methodological approach could provide a framework for adjusting ITE trip-generation estimates in smart-growth areas throughout the United States.